Research on Financing Risk Assessment and Optimization of Digital Economy Enterprises Combined with Deep Learning Technology
Bibliographic record
Abstract
In the past few years, the meteoric rise of artificial intelligence, especially the pervasive adoption of deep learning, has sparked a boom in digital economy enterprises. These companies have emerged left and right, breathing new vitality into economic growth and transforming the landscape of modern business. However, due to rapid development and innovation, digital economy firms confront numerous risks and obstacles during the financing process. This article focuses on how deep learning technology can evaluate and optimize the financing risks of digital economy firms, with the goal of providing an efficient and accurate risk control approach to support enterprises’ healthy and long-term growth. Deep learning technology, as a strong data analysis tool, has demonstrated extraordinary potential in the context of financing risk assessment. This paper develops a deep neural network model for assessing financing risk by examining the financing environment and risk characteristics encountered by digital economy firms. To begin, essential input data such as financial statistics, market performance, and the enterprise's management team background are retrieved from past financing situations. Second, create deep learning structures such as multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) to mine enormous volumes of data and properly identify financial threats that businesses may face. Furthermore, the model's output results can be used to optimize the enterprise's finance strategy, such as recommending reducing the financing amount, prolonging the financing cycle, or altering the financing structure in high-risk situations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".